A Performance Study of Local Outlier Detection Methods for Mineral Exploration with Geochemical Compositional Data

In exploration geochemistry, mineral deposits are typically characterised by local changes of the analysed chemical composition, where enrichment of the targeted elements is expected. Local changes can also be found with local outlier detection methods, which are multivariate methods for outlier identification that incorporate the spatial neighbourhood of the samples. It is essential that geochemical data are treated as compositional data, and the requirements for their treatment depend on the specific local outlier detection method. We demonstrate how prominent local outlier detection methods can be used for mineral exploration with geochemical data that vary in scale, in the sampling density, and in data quality. The methods are compared based on known mineralisations, and recommendations for their usefulness are provided.